A Low Latency Adaptive Video Streaming Framework To Control The Congestion And Reduce The Switching Times Between Quality Levels

Video streaming over the Internet is considered the most essential communication technology in recent years, especially with the spread of the SARS-CoV2 pandemic where the communication between people and the work via online videos. This technology faces various challenges that may be obstacles for the customer and the service provider to deliver videos with a flexible quality and without interruption. The high congestion value of the frames on the buffer of the client is one of these challenges. Consequently, the video latency is increased, and thus reduced the video throughput with more interruptions and low quality. The paper is suggested an adaptive video streaming framework by introducing a low latency in an online application to control the congestion and reduce switching times between levels to make video streaming more seamless. The low latency scheme aims to increase the bitrates to optimal values to avoid the congestion of frames on the buffer with a minimum delay value. Analyzing the video load based on three Parameters: latency, Buffering, and throughput of the dynamic system will control the congestion to reduce the switching between quality levels and consequently improve the performance.

Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt.

COVID-19 is spreading within the sort of an enormous epidemic for the globe. This epidemic infects a lot of individuals in Egypt. The World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory spray. On these days, Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this epidemic. In this paper displayed, the real database of COVID-19 for Egypt has been analysed from February 15, 2020, to June 15, 2020, and predicted with the number of patients that will be infected with COVID-19, and estimated the epidemic final size. Several regression analysis models have been applied for data analysis of COVID-19 of Egypt. In this study, we've been applied seven regression analysis-based models that are exponential polynomial, quadratic, third-degree, fourth-degree, fifth-degree, sixth-degree, and logit growth respectively for the COVID-19 dataset. Thus, the exponential, fourth-degree, fifth-degree, and sixth-degree polynomial regression models are excellent models specially fourth-degree model that will help the government preparing their procedures for one month. In addition, we have applied the well-known logit growth regression model and we obtained the following epidemiological insights: Firstly, the epidemic peak could possibly reach at 22-June 2020 and final time of epidemic at 8-September 2020. Secondly, the final total size for cases 1.6676E+05 cases. The action from government of interevent over a relatively long interval is necessary to minimize the final epidemic size.